Updates an existing study. Use this to increase interview slots, add/update media, or modify the interview guide.
AI agents use update_study to create or update resources in Mcp Usercall — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Mcp Usercall environment.
The tool creates or modifies data (the study configuration and its parameters) but does not delete, execute arbitrary code, or move money. Updates are reversible—prior versions can be restored or the changes undone. This fits the Write category.
From the tool's definition Tool description states it 'Updates an existing study' and explicitly mentions modifying parameters like 'increase interview slots, add/update media, or modify the interview guide.' These are reversible modifications to stored data structures.
Documented attack patterns abuse exactly the kind of access update_study gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Mcp Usercall, and nothing reaches the server without passing your rules. This is the rule we recommend for update_study:
{
"version": "1",
"default": "deny",
"tools": {
"update_study": {
"limits": [
{
"counter": "update_study_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}update_study stays usable, but capped — an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
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Updates an existing study. Use this to increase interview slots, add/update media, or modify the interview guide. It is categorised as a Write tool in the Mcp Usercall MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Mcp Usercall MCP server in PolicyLayer and add a rule for update_study: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Usercall. Nothing to install.
update_study is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the update_study rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for update_study. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
update_study is provided by the Mcp Usercall MCP server (junetic/usercall-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Mcp Usercall, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
5 Mcp Usercall tools catalogued and risk-classified — across an index of 43,000+ MCP servers.